Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic...

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Main Authors: Hassan, S., Khosravi, A., Jaafar, J.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.26298 /
Published: Association for Computing Machinery, Inc 2015
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926211661&doi=10.1145%2f2701126.2701177&partnerID=40&md5=c4ec501f2cd7ab601d98ecd11e5a8fb0
http://eprints.utp.edu.my/26298/
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spelling utp-eprints.262982021-08-30T07:06:45Z Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting Hassan, S. Khosravi, A. Jaafar, J. Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models. Association for Computing Machinery, Inc 2015 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926211661&doi=10.1145%2f2701126.2701177&partnerID=40&md5=c4ec501f2cd7ab601d98ecd11e5a8fb0 Hassan, S. and Khosravi, A. and Jaafar, J. (2015) Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting. In: UNSPECIFIED. http://eprints.utp.edu.my/26298/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.
format Conference or Workshop Item
author Hassan, S.
Khosravi, A.
Jaafar, J.
spellingShingle Hassan, S.
Khosravi, A.
Jaafar, J.
Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
author_sort Hassan, S.
title Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
title_short Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
title_full Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
title_fullStr Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
title_full_unstemmed Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
title_sort training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
publisher Association for Computing Machinery, Inc
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926211661&doi=10.1145%2f2701126.2701177&partnerID=40&md5=c4ec501f2cd7ab601d98ecd11e5a8fb0
http://eprints.utp.edu.my/26298/
_version_ 1741197115759001600
score 11.62408

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